##################################################
# This file is mostly re-used from:
# https://github.com/facebookresearch/deit/blob/main/models_v2.py
##################################################

# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.

from functools import partial

import torch
import torch.nn as nn
import torch.nn.functional as F
from timm.models.vision_transformer import Mlp, PatchEmbed, _cfg
from timm.models.layers import DropPath, to_2tuple, trunc_normal_


__all__ = [
    "deit3_vitsmall",
    "deit3_vitbase",
    "deit3_vitlarge",
]


class Attention(nn.Module):
    # taken from https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/vision_transformer.py
    def __init__(
        self,
        dim,
        num_heads=8,
        qkv_bias=False,
        qk_scale=None,
        attn_drop=0.0,
        proj_drop=0.0,
    ):
        super().__init__()
        self.num_heads = num_heads
        head_dim = dim // num_heads
        self.scale = qk_scale or head_dim**-0.5

        self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias)
        self.attn_drop = nn.Dropout(attn_drop)
        self.proj = nn.Linear(dim, dim)
        self.proj_drop = nn.Dropout(proj_drop)

    def forward(self, x):
        B, N, C = x.shape
        qkv = (
            self.qkv(x)
            .reshape(B, N, 3, self.num_heads, C // self.num_heads)
            .permute(2, 0, 3, 1, 4)
        )
        q, k, v = qkv[0], qkv[1], qkv[2]

        q = q * self.scale

        attn = q @ k.transpose(-2, -1)
        attn = attn.softmax(dim=-1)
        attn = self.attn_drop(attn)

        x = (attn @ v).transpose(1, 2).reshape(B, N, C)
        x = self.proj(x)
        x = self.proj_drop(x)
        return x


class Block(nn.Module):
    # taken from https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/vision_transformer.py
    def __init__(
        self,
        dim,
        num_heads,
        mlp_ratio=4.0,
        qkv_bias=False,
        qk_scale=None,
        drop=0.0,
        attn_drop=0.0,
        drop_path=0.0,
        act_layer=nn.GELU,
        norm_layer=nn.LayerNorm,
        Attention_block=Attention,
        Mlp_block=Mlp,
        init_values=1e-4,
    ):
        super().__init__()
        self.norm1 = norm_layer(dim)
        self.attn = Attention_block(
            dim,
            num_heads=num_heads,
            qkv_bias=qkv_bias,
            qk_scale=qk_scale,
            attn_drop=attn_drop,
            proj_drop=drop,
        )
        # NOTE: drop path for stochastic depth, we shall see if this is better than dropout here
        self.drop_path = DropPath(drop_path) if drop_path > 0.0 else nn.Identity()
        self.norm2 = norm_layer(dim)
        mlp_hidden_dim = int(dim * mlp_ratio)
        self.mlp = Mlp_block(
            in_features=dim,
            hidden_features=mlp_hidden_dim,
            act_layer=act_layer,
            drop=drop,
        )

    def forward(self, x):
        x = x + self.drop_path(self.attn(self.norm1(x)))
        x = x + self.drop_path(self.mlp(self.norm2(x)))
        return x


class Layer_scale_init_Block(nn.Module):
    # taken from https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/vision_transformer.py
    # with slight modifications
    def __init__(
        self,
        dim,
        num_heads,
        mlp_ratio=4.0,
        qkv_bias=False,
        qk_scale=None,
        drop=0.0,
        attn_drop=0.0,
        drop_path=0.0,
        act_layer=nn.GELU,
        norm_layer=nn.LayerNorm,
        Attention_block=Attention,
        Mlp_block=Mlp,
        init_values=1e-4,
    ):
        super().__init__()
        self.norm1 = norm_layer(dim)
        self.attn = Attention_block(
            dim,
            num_heads=num_heads,
            qkv_bias=qkv_bias,
            qk_scale=qk_scale,
            attn_drop=attn_drop,
            proj_drop=drop,
        )
        # NOTE: drop path for stochastic depth, we shall see if this is better than dropout here
        self.drop_path = DropPath(drop_path) if drop_path > 0.0 else nn.Identity()
        self.norm2 = norm_layer(dim)
        mlp_hidden_dim = int(dim * mlp_ratio)
        self.mlp = Mlp_block(
            in_features=dim,
            hidden_features=mlp_hidden_dim,
            act_layer=act_layer,
            drop=drop,
        )
        self.gamma_1 = nn.Parameter(init_values * torch.ones((dim)), requires_grad=True)
        self.gamma_2 = nn.Parameter(init_values * torch.ones((dim)), requires_grad=True)

    def forward(self, x):
        x = x + self.drop_path(self.gamma_1 * self.attn(self.norm1(x)))
        x = x + self.drop_path(self.gamma_2 * self.mlp(self.norm2(x)))
        return x


class Layer_scale_init_Block_paralx2(nn.Module):
    # taken from https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/vision_transformer.py
    # with slight modifications
    def __init__(
        self,
        dim,
        num_heads,
        mlp_ratio=4.0,
        qkv_bias=False,
        qk_scale=None,
        drop=0.0,
        attn_drop=0.0,
        drop_path=0.0,
        act_layer=nn.GELU,
        norm_layer=nn.LayerNorm,
        Attention_block=Attention,
        Mlp_block=Mlp,
        init_values=1e-4,
    ):
        super().__init__()
        self.norm1 = norm_layer(dim)
        self.norm11 = norm_layer(dim)
        self.attn = Attention_block(
            dim,
            num_heads=num_heads,
            qkv_bias=qkv_bias,
            qk_scale=qk_scale,
            attn_drop=attn_drop,
            proj_drop=drop,
        )
        # NOTE: drop path for stochastic depth, we shall see if this is better than dropout here
        self.attn1 = Attention_block(
            dim,
            num_heads=num_heads,
            qkv_bias=qkv_bias,
            qk_scale=qk_scale,
            attn_drop=attn_drop,
            proj_drop=drop,
        )
        self.drop_path = DropPath(drop_path) if drop_path > 0.0 else nn.Identity()
        self.norm2 = norm_layer(dim)
        self.norm21 = norm_layer(dim)
        mlp_hidden_dim = int(dim * mlp_ratio)
        self.mlp = Mlp_block(
            in_features=dim,
            hidden_features=mlp_hidden_dim,
            act_layer=act_layer,
            drop=drop,
        )
        self.mlp1 = Mlp_block(
            in_features=dim,
            hidden_features=mlp_hidden_dim,
            act_layer=act_layer,
            drop=drop,
        )
        self.gamma_1 = nn.Parameter(init_values * torch.ones((dim)), requires_grad=True)
        self.gamma_1_1 = nn.Parameter(
            init_values * torch.ones((dim)), requires_grad=True
        )
        self.gamma_2 = nn.Parameter(init_values * torch.ones((dim)), requires_grad=True)
        self.gamma_2_1 = nn.Parameter(
            init_values * torch.ones((dim)), requires_grad=True
        )

    def forward(self, x):
        x = (
            x
            + self.drop_path(self.gamma_1 * self.attn(self.norm1(x)))
            + self.drop_path(self.gamma_1_1 * self.attn1(self.norm11(x)))
        )
        x = (
            x
            + self.drop_path(self.gamma_2 * self.mlp(self.norm2(x)))
            + self.drop_path(self.gamma_2_1 * self.mlp1(self.norm21(x)))
        )
        return x


class Block_paralx2(nn.Module):
    # taken from https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/vision_transformer.py
    # with slight modifications
    def __init__(
        self,
        dim,
        num_heads,
        mlp_ratio=4.0,
        qkv_bias=False,
        qk_scale=None,
        drop=0.0,
        attn_drop=0.0,
        drop_path=0.0,
        act_layer=nn.GELU,
        norm_layer=nn.LayerNorm,
        Attention_block=Attention,
        Mlp_block=Mlp,
        init_values=1e-4,
    ):
        super().__init__()
        self.norm1 = norm_layer(dim)
        self.norm11 = norm_layer(dim)
        self.attn = Attention_block(
            dim,
            num_heads=num_heads,
            qkv_bias=qkv_bias,
            qk_scale=qk_scale,
            attn_drop=attn_drop,
            proj_drop=drop,
        )
        # NOTE: drop path for stochastic depth, we shall see if this is better than dropout here
        self.attn1 = Attention_block(
            dim,
            num_heads=num_heads,
            qkv_bias=qkv_bias,
            qk_scale=qk_scale,
            attn_drop=attn_drop,
            proj_drop=drop,
        )
        self.drop_path = DropPath(drop_path) if drop_path > 0.0 else nn.Identity()
        self.norm2 = norm_layer(dim)
        self.norm21 = norm_layer(dim)
        mlp_hidden_dim = int(dim * mlp_ratio)
        self.mlp = Mlp_block(
            in_features=dim,
            hidden_features=mlp_hidden_dim,
            act_layer=act_layer,
            drop=drop,
        )
        self.mlp1 = Mlp_block(
            in_features=dim,
            hidden_features=mlp_hidden_dim,
            act_layer=act_layer,
            drop=drop,
        )

    def forward(self, x):
        x = (
            x
            + self.drop_path(self.attn(self.norm1(x)))
            + self.drop_path(self.attn1(self.norm11(x)))
        )
        x = (
            x
            + self.drop_path(self.mlp(self.norm2(x)))
            + self.drop_path(self.mlp1(self.norm21(x)))
        )
        return x


class hMLP_stem(nn.Module):
    """hMLP_stem: https://arxiv.org/pdf/2203.09795.pdf
    taken from https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/vision_transformer.py
    with slight modifications
    """

    def __init__(
        self,
        img_size=224,
        patch_size=16,
        in_chans=3,
        embed_dim=768,
        norm_layer=nn.SyncBatchNorm,
    ):
        super().__init__()
        img_size = to_2tuple(img_size)
        patch_size = to_2tuple(patch_size)
        num_patches = (img_size[1] // patch_size[1]) * (img_size[0] // patch_size[0])
        self.img_size = img_size
        self.patch_size = patch_size
        self.num_patches = num_patches
        self.proj = torch.nn.Sequential(
            *[
                nn.Conv2d(in_chans, embed_dim // 4, kernel_size=4, stride=4),
                norm_layer(embed_dim // 4),
                nn.GELU(),
                nn.Conv2d(embed_dim // 4, embed_dim // 4, kernel_size=2, stride=2),
                norm_layer(embed_dim // 4),
                nn.GELU(),
                nn.Conv2d(embed_dim // 4, embed_dim, kernel_size=2, stride=2),
                norm_layer(embed_dim),
            ]
        )

    def forward(self, x):
        B, C, H, W = x.shape
        x = self.proj(x).flatten(2).transpose(1, 2)
        return x


class vit_models(nn.Module):
    """Vision Transformer with LayerScale (https://arxiv.org/abs/2103.17239) support
    taken from https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/vision_transformer.py
    with slight modifications
    """

    def __init__(
        self,
        img_size=224,
        patch_size=16,
        in_chans=3,
        num_classes=1000,
        embed_dim=768,
        depth=12,
        num_heads=12,
        mlp_ratio=4.0,
        qkv_bias=False,
        qk_scale=None,
        drop_rate=0.0,
        attn_drop_rate=0.0,
        drop_path_rate=0.0,
        norm_layer=nn.LayerNorm,
        global_pool=None,
        block_layers=Block,
        Patch_layer=PatchEmbed,
        act_layer=nn.GELU,
        Attention_block=Attention,
        Mlp_block=Mlp,
        dpr_constant=True,
        init_scale=1e-4,
        mlp_ratio_clstk=4.0,
        patch_aggregation="cls",  # "cls" or "gap"
        **kwargs
    ):
        super().__init__()

        self.dropout_rate = drop_rate

        assert patch_aggregation in [
            "cls",
            "gap",
        ], "patch_aggregation should be cls or gap, but got {}".format(
            patch_aggregation
        )
        self.patch_aggregation = patch_aggregation

        self.num_classes = num_classes
        self.num_features = self.embed_dim = embed_dim

        self.patch_embed = Patch_layer(
            img_size=img_size,
            patch_size=patch_size,
            in_chans=in_chans,
            embed_dim=embed_dim,
        )
        num_patches = self.patch_embed.num_patches

        self.cls_token = nn.Parameter(torch.zeros(1, 1, embed_dim))

        self.pos_embed = nn.Parameter(torch.zeros(1, num_patches, embed_dim))

        dpr = [drop_path_rate for i in range(depth)]
        self.blocks = nn.ModuleList(
            [
                block_layers(
                    dim=embed_dim,
                    num_heads=num_heads,
                    mlp_ratio=mlp_ratio,
                    qkv_bias=qkv_bias,
                    qk_scale=qk_scale,
                    drop=0.0,
                    attn_drop=attn_drop_rate,
                    drop_path=dpr[i],
                    norm_layer=norm_layer,
                    act_layer=act_layer,
                    Attention_block=Attention_block,
                    Mlp_block=Mlp_block,
                    init_values=init_scale,
                )
                for i in range(depth)
            ]
        )

        self.norm = norm_layer(embed_dim)

        self.feature_info = [dict(num_chs=embed_dim, reduction=0, module="head")]
        self.head = (
            nn.Linear(embed_dim, num_classes) if num_classes > 0 else nn.Identity()
        )

        trunc_normal_(self.pos_embed, std=0.02)
        trunc_normal_(self.cls_token, std=0.02)
        self.apply(self._init_weights)

    def _init_weights(self, m):
        if isinstance(m, nn.Linear):
            trunc_normal_(m.weight, std=0.02)
            if isinstance(m, nn.Linear) and m.bias is not None:
                nn.init.constant_(m.bias, 0)
        elif isinstance(m, nn.LayerNorm):
            nn.init.constant_(m.bias, 0)
            nn.init.constant_(m.weight, 1.0)

    @torch.jit.ignore
    def no_weight_decay(self):
        return {"pos_embed", "cls_token"}

    def get_classifier(self):
        return self.head

    def get_num_layers(self):
        return len(self.blocks)

    def reset_classifier(self, num_classes, global_pool=""):
        self.num_classes = num_classes
        self.head = (
            nn.Linear(self.embed_dim, num_classes) if num_classes > 0 else nn.Identity()
        )

    def forward_features(self, x):
        B = x.shape[0]
        x = self.patch_embed(x)

        cls_tokens = self.cls_token.expand(B, -1, -1)

        x = x + self.pos_embed

        x = torch.cat((cls_tokens, x), dim=1)

        for i, blk in enumerate(self.blocks):
            x = blk(x)

        x_norm = self.norm(x)

        return {
            "x_norm_clstoken": x_norm[:, 0],
            "x_norm_patchtokens": x_norm[:, 1:],
            "x_prenorm": x,
            "x_prenorm_clstoken": x[:, 0],
            "x_prenorm_patchtokens": x[:, 1:],
        }

    def forward(self, x):
        x = self.forward_features(x)
        x = x["x_norm_clstoken"]

        if self.dropout_rate:
            x = F.dropout(x, p=float(self.dropout_rate), training=self.training)

        x = self.head(x)

        return x


def deit3_vitsmall(img_size=224, patch_size=16, patch_aggregation="cls", **kwargs):
    model = vit_models(
        img_size=img_size,
        patch_size=patch_size,
        embed_dim=384,
        depth=12,
        num_heads=6,
        mlp_ratio=4,
        qkv_bias=True,
        norm_layer=partial(nn.LayerNorm, eps=1e-6),
        block_layers=Layer_scale_init_Block,
        patch_aggregation=patch_aggregation,
        **kwargs
    )
    model.default_cfg = _cfg()
    return model


def deit3_vitbase(img_size=224, patch_size=16, patch_aggregation="cls", **kwargs):
    model = vit_models(
        img_size=img_size,
        patch_size=patch_size,
        embed_dim=768,
        depth=12,
        num_heads=12,
        mlp_ratio=4,
        qkv_bias=True,
        norm_layer=partial(nn.LayerNorm, eps=1e-6),
        block_layers=Layer_scale_init_Block,
        patch_aggregation=patch_aggregation,
        **kwargs
    )
    model.default_cfg = _cfg()
    return model


def deit3_vitlarge(img_size=224, patch_size=16, patch_aggregation="cls", **kwargs):
    model = vit_models(
        img_size=img_size,
        patch_size=patch_size,
        embed_dim=1024,
        depth=24,
        num_heads=16,
        mlp_ratio=4,
        qkv_bias=True,
        norm_layer=partial(nn.LayerNorm, eps=1e-6),
        block_layers=Layer_scale_init_Block,
        patch_aggregation=patch_aggregation,
        **kwargs
    )
    model.default_cfg = _cfg()
    return model
